Walmart, Amazon Take Divergent Approaches to Agentic Commerce

Walmart and Amazon are taking apparently opposite approaches to agentic commerce. Walmart leans to towards an “open” approach while Amazon prefers a more-closed approach. 

Walmart is trying to make its products available wherever consumers choose to shop with AI, partnering with ChatGPT and Gemini, while also building its own agentic agent Sparky,  while Amazon is trying to ensure that AI shopping remains inside an environment Amazon controls by barring most third-party access and relying on its own Rufus agent and other related features.  

It’s an old set of choices. Should an incumbent embrace a new technology platform that may weaken its control over customers, or defend its existing ecosystem and risk losing customers to the new platform?

Neither strategy is universally successful or applicable, though. Much hinges on a firm’s position within an industry. Openness tends to help a company when the new technology shifts value toward a platform or distribution channel it does not control. 

Closed ecosystems tend to work better when the company can make its own platform the preferred destination. 

Open approaches also tend to enable new competition, while closed approaches might tend to favor incumbents with market power. 

But it also is fair to say that both firms attempt to control the interface, the data, and the transaction.

Strategic issue	Walmart's calculation	Amazon's calculation
Customer acquisition	Third-party AI can introduce Walmart to consumers who do not begin their shopping journey on Walmart.com.	Outside agents could divert existing Amazon customers and reduce the importance of Amazon's own app and website.
Competitive position	As a retailer competing against Amazon, Walmart benefits if AI makes retailer choice more fluid.	Amazon benefits from the frictionless integration of its catalog, reviews, payments, Prime benefits, and delivery network.
Customer data	Cooperation can provide incremental demand, although Walmart risks losing some browsing and intent data to AI intermediaries.	Outside agents could capture valuable signals about what customers want, compare, reject, and ultimately buy.
Advertising and sponsored listings	AI recommendations could create another route to product discovery, but may weaken Walmart's control over advertising inventory and ranking.	Amazon's sponsored product placements and retail-media economics could be undermined if an independent agent chooses products using its own ranking criteria.
Price competition	Walmart can compete on price, convenience, grocery, and physical-store reach if agents make comparisons easier.	Agents that systematically compare alternatives could expose price differences and weaken Amazon's ability to retain customers through convenience and ecosystem benefits.
Transaction economics	A new channel is attractive if incremental sales outweigh integration costs and any commissions or loss of control.	Keeping transactions in-house protects more of the economics and preserves control over checkout, customer service, and fulfillment.
Principal risk	Becoming a supplier to someone else's AI platform rather than owning the customer relationship.	Protecting the current platform too aggressively and allowing a new AI intermediary to become the consumer's default shopping destination.

Beyond all that, there can be other issues driving a policy choice. Walmart, for example, encountered issues with a native ChatGPT checkout experience, and now integrates its own Sparky agent. And Amazon might eventually allow some access by external agents, if it can keep control of the customer relationship, or conversely, if it finds it must cede some control to protect sales volume.

Historically, computer operating systems, browsers and mobile phones illustrate cases where distribution, customer relationships or the economic value of the ecosystem shaped choices, generally involving choices between degrees of openness versus closed approaches. 

Historical case	Strategic choice	What happened	Lesson for Walmart and Amazon
IBM PC vs. the Microsoft/Intel ecosystem (1980s–1990s)	IBM initially benefited from a PC architecture built around widely available components and outside software. Microsoft and Intel occupied increasingly powerful positions within that ecosystem.	IBM helped establish a market whose economics ultimately favored independent software and chip platforms. IBM lost much of its control over the architecture it had helped popularize. IBM history of e-business; U.S. DOJ Microsoft findings.	A company can create a successful ecosystem without capturing the largest share of its eventual value. Walmart could help normalize AI-mediated shopping while an AI provider captures customer attention and economic rents.
IBM OS/2 vs. Microsoft Windows (late 1980s–1990s)	IBM and Microsoft initially cooperated on OS/2, then diverged. Microsoft concentrated on Windows, while IBM continued to develop OS/2.	Windows gained a decisive advantage from software availability, hardware compatibility, and distribution through PC manufacturers. OS/2 struggled to match that ecosystem. OS/2 history.	Cooperation alone does not win. A company must attract developers, users, and complementary services around its platform. Walmart's partnerships will matter most if they generate durable customer relationships and sales.
Microsoft Internet Explorer vs. Netscape (1990s)	Microsoft initially treated the browser as a threat to Windows and responded by integrating Internet Explorer with Windows and distributing it without a separate charge.	Internet Explorer displaced Netscape's leading position. The U.S. antitrust case documented Microsoft's efforts to protect its operating-system monopoly from the emerging browser platform. U.S. DOJ findings.	Amazon has a rational reason to defend its shopping interface. But incumbents may also use integration and distribution advantages to defend a threatened business model, potentially inviting regulatory scrutiny.
AOL's walled garden vs. the open Internet (1990s–2000s)	AOL initially offered a curated online environment; the wider Internet offered access to independently operated websites and services.	The open web became the broader platform for online activity. AOL later adapted its business rather than retaining its former position as the primary gateway to online life. IBM's history of e-business.	A retailer's controlled environment can be attractive until consumers prefer a more convenient intermediary that searches across competing destinations. Amazon must consider whether its walls add enough value to offset the friction they impose.
Apple iPhone vs. Android (2007 onward)	Apple chose a tightly integrated hardware and software ecosystem. Android enabled a broader network of device manufacturers to participate in a shared platform.	Both strategies succeeded, but in different ways: Apple captured substantial value through integration and premium devices, while Android spread across a much wider range of manufacturers. Apple's developer ecosystem; Android.	This is a crucial counterexample to the idea that openness always wins. Amazon could preserve a highly profitable closed ecosystem if its convenience, trust, and fulfillment advantages remain compelling.
The open web vs. proprietary online services (1990s onward)	Proprietary services controlled the user experience; web standards allowed independent sites, search engines, and new intermediaries to emerge.	Open protocols enabled new categories of businesses and made it possible for companies such as Google to become major gateways to information without owning all the underlying content.	An AI shopping agent could become the equivalent of a search engine for purchases, directing demand across retailers rather than requiring consumers to visit each retailer first.

It might be tempting to conclude that “open” tends to win, but not always. Apple’s  example suggests openness is not a universal rule. Closed ecosystems can thrive when consumers value a coherent experience enough to accept the restrictions. 

And “control” might exist in a few different parts of the value chain:

• The user's AI agent, which understands intent, compares products, recommends and negotiates

• Retailers and their systems who supply product catalogs, prices, inventory, promotions, loyalty and checkout experiences

• Physical commerce infrastructure including warehouses, stores, picking, delivery, returns and customer service. 

It is possible that ultimate success for Walmart or Amazon will hinge on dominance in a couple, but not all, of these parts of the value chain. And either open or closed strategies could work. 

Indicator	Evidence favoring Walmart's strategy	Evidence favoring Amazon's strategy
Where shopping begins	A rising share of purchase journeys originates in general-purpose AI assistants.	Consumers continue to begin most high-intent shopping journeys on Amazon.
Incremental customer economics	AI referrals generate profitable new customers and larger baskets at reasonable acquisition cost.	Amazon's own assistant increases conversion, frequency, and retention more effectively than external agents.
Who controls recommendations	Independent agents can compare retailers and rank products without retailer restrictions.	Retailer-controlled agents provide sufficiently superior recommendations, loyalty benefits, and convenience to keep customers inside their ecosystems.
Advertising and monetization	Retailers can monetize AI-driven demand without surrendering too much margin or data.	Amazon retains valuable sponsored placements, purchase-intent data, and other retail-media revenue within its own environment.
Transaction reliability	Common standards make inventory, payment, substitution, delivery, and returns work across retailers.	Complex transactions work materially better inside each retailer's integrated systems.

Walmart has an incentive to open distribution because it can gain from a change in the shopping gateway. 

Amazon, on the other hand, has an incentive to control distribution because it is the existing gateway.

But much hinges on where users experience value. If starting a shopping journey inside Amazon continues to make sense, Amazon wins. If users start to trust their agents, then perhaps Walmart wins. And it seems inevitable that neither Amazon nor Walmart approaches win all the time. 

It might matter whether the user values selection across retailers (perhaps choices are narrower but price differences can be significant), compared to the value of curation when the product universe is large. 

The key variables might include:

• how much comparison matters

•  how costly a mistake is

• how much context the agent needs

• whether the retailer has a unique advantage in price, assortment, or fulfillment.

Not every shopping experience favors each strategy. 

Shopping experience	Likely advantage	Why
Routine grocery replenishment	Walmart openness	An agent can compare prices across grocers, assemble a basket, and optimize substitutions, store proximity, and delivery. Walmart's stores and grocery scale are valuable if the agent can access them.
Repeat purchases of known products	Amazon curation, with a caveat	One-click reordering, purchase history, reviews, and reliable delivery make Amazon convenient. But if an independent agent can automatically find the cheapest equivalent across retailers, Walmart benefits from openness.
Staples and commodity products	Walmart openness	For paper towels, detergent, batteries, and pantry staples, product differences may be small relative to price, availability, and delivery cost. Cross-retailer comparison is valuable.
Novelty and product discovery	Amazon curation	A broad catalog, customer reviews, recommendations, and product comparisons can help consumers discover unfamiliar products. A strong independent AI agent could, however, reproduce much of this capability across retailers.
Rare, complex purchases	Amazon curation, potentially	For cameras, specialized tools, electronics, or hobby equipment, product specifications, compatibility, and reviews matter. Amazon has extensive product information and shopping history. An independent agent may win if it searches specialist retailers and manufacturer sources more comprehensively.
Urgent purchases	Depends on local fulfillment	The winning retailer is the one that can actually deliver the right item fastest. Walmart's physical-store network may be decisive for local essentials; Amazon may win on products already stocked in its fulfillment network.
Large, mixed baskets	Walmart openness	An agent can optimize the entire basket across retailers, rather than selecting the best individual product at one retailer. Walmart's grocery, household, pharmacy-related merchandise, and general merchandise breadth can help.
Premium or highly differentiated goods	Depends on brand access	Product authenticity, warranties, authorized distribution, expert advice, and service may matter more than the lowest price. Neither Walmart's openness nor Amazon's curation automatically wins.
Gift shopping	Open agents, potentially	An agent can search multiple stores using recipient interests, budget, delivery deadlines, and gift availability. Amazon's reviews and recommendations still help, but cross-retailer discovery may offer more distinctive choices.
Fashion, beauty, and home décor	Mixed	Personal taste, visual discovery, sizing, returns, and brand identity complicate pure price optimization. Retailer-specific expertise can matter, but a cross-retailer agent could broaden discovery and compare return policies.

And different types of purchases might create value in ways that either approach can tend to capture:

• Frequency favors whichever platform has the best history, automation, and fulfillment.

• Commodity products favor open, multi-retailer agents.

• Decision complexity might favor the agent with the best data and reasoning

• Fulfilment (delivery speed, returns, warranties, local availability matter favors the provider with  strongest operational capabilities.

And there also are the issues around user preferences. Some users will want to delegate more; some less. Some will trust agents more; some less. 

The point: it is not certain which approach is “better” or “more successful” long term. Both can work, sometimes, for some purchases.

What Future for Neoclouds?

Investors arguably have lots of reasons for wondering how long the neocloud market opportunity lasts, and which parts of the business case are more durable, assuming demand and revenue growth hold up. 

The simple “we have graphic processor units available when the hyperscalers do not” opportunity is narrowing, but is not gone. And the market appears to be shifting from simple GPU “scarcity” to other dimensions. Scarcity created the market opportunity, but enduring businesses need more than bare-metal GPU rental, according to analysts at McKinsey. 

source: McKinsey

Opportunity	What creates the opening	Assessment
Scarcity-based GPU rental	Customers cannot obtain suitable GPUs quickly enough from incumbent clouds	The most vulnerable proposition as supply expands and customers gain alternatives
Wholesale capacity provision	Hyperscalers and AI labs purchase dedicated clusters from third parties instead of developing everything themselves	Still a substantial opportunity, but highly dependent on financing, delivery, and contract economics
Specialized AI cloud services	Customers value performance, software, support, sovereignty, or deployment flexibility beyond hardware availability	Potentially durable, but requires capabilities that are harder to build than a GPU fleet

Concern about a potential “AI bubble bursting” aside, observers do not agree about the demand-supply balance. Gartner argues “we have more compute capacity than we have demand,” arguing that commercial returns are not yet sufficient to justify the build-out. 

Analysts at UBS argue that compute remains scarce and points to rising rental rates for older GPUs as evidence.

Actual procurement commitments also show continued demand for third-party infrastructure. CoreWeave announced an approximately $21 billion expanded Meta agreement extending through December 2032, while Nebius reported in August that it had delivered all capacity tranches under its Microsoft contract and was progressing capacity supporting another Meta agreement, for example.

The bigger concern seems to be whether neocloud service providers can earn a sustainable profit, given capital investment requirements, the cost of financing and the revenue growth rates. 

 “Is compute still scarce?” Yes. But the issues are more “which compute, available when, for which workload, at what economically sustainable price and profit margin?”

A neocloud compute services provider  can have real customers, a growing backlog, and strong revenue growth while still struggling to earn an adequate return on capital.

Perhaps generally speaking, some models seem safer than others. Bare metal services built using borrowed money seem most vulnerable. Providers with guaranteed and adequate energy supply, value-added features or regulatory advantages might be better positioned. 

Provider profile	Relative exposure	Why
Undifferentiated GPU renter relying on scarcity pricing	High	Few defenses against repricing, lower utilization, or customer migration
Highly leveraged operator building before securing dependable demand	High	Financing and deployment risks compound one another
Wholesale supplier with strong counterparties and well-matched financing	More defensible, but contract-dependent	Demand visibility helps; margins and residual asset risk still matter
Operator controlling attractive power, sites, and delivery capabilities	Potentially durable	Can solve infrastructure bottlenecks that chip availability alone does not resolve
Specialized AI cloud with demonstrable software or operating advantages	Potentially durable	Can compete on customer outcomes rather than inventory
Sovereign or regulated-market specialist	Potentially durable	Location, governance, and independence can matter beyond price
Provider assuming old fleets will automatically find enterprise customers	Unproven	Residual demand is possible, but neither utilization nor pricing is guaranteed

All that noted, Gartner’s prediction that 80 percent of operating neoclouds will ultimately disappear simply recognizes the arguably-inevitable consolidation of the market. That happens with every new market. 

Computing market	Early proliferation	the shakeout	Later market structure or outcome
Commercial mainframes	1950s–1960s: IBM competed with Burroughs, UNIVAC, NCR, Control Data, Honeywell, GE, and RCA—the “Seven Dwarfs.”	IBM’s System/360 strengthened its platform position. GE sold its computer business to Honeywell in 1970; RCA exited in 1971. Computer History	An IBM-led market with a smaller set of competitors—not eight equally sustainable platforms. This is a case of concentration around an already powerful incumbent, rather than an initially level playing field. Computer History
Minicomputers	1960s–1980s: DEC, Data General, Scientific Data Systems, HP, and other suppliers developed smaller, less expensive alternatives to mainframes. ETHW	Competition from newer computing architectures eroded the category. Compaq acquired DEC in 1998; EMC acquired Data General in 1999; HP acquired Compaq in 2002. ethw	Many independent suppliers disappeared, but not because the original market reached a stable end state. The category itself was largely superseded. ethw
Personal-computer hardware	Late 1970s–1980s: Apple, Commodore, Tandy, Atari, Osborne, IBM, Compaq, and numerous compatible-system suppliers.  Doncio, U.S. Navy	IBM-compatible systems became the dominant standard; mergers and exits reduced the number of major independent manufacturers. HP acquired Compaq in 2002, and Lenovo acquired IBM’s PC business in 2005.	In 2025, Lenovo, HP, and Dell accounted for approximately 63.8% of worldwide PC shipments; the top six accounted for approximately 86.1%. Smaller suppliers remained, but leadership was concentrated. gartner
Smartphone operating systems	2000s–early 2010s: Symbian, BlackBerry, Windows Mobile/Windows Phone, Android, and iOS competed for users and developers. Wikipedia	Earlier platforms lost relevance as Android and iOS gained adoption. Symbian lost its leadership position; BlackBerry and Microsoft’s mobile platforms became marginal. weforum	Android and iOS accounted for 99.9% of worldwide smartphone sales to end users in 2017. This describes operating-system concentration, not concentration among handset manufacturers. weforum
Cloud infrastructure services	From the 2000s onward, the market developed a broad field of global, regional, and specialized providers. It subsequently became strongly led by AWS, Microsoft, and Google. srgresearch	The observable result is concentration at the top rather than disappearance of all smaller suppliers. Synergy continues to identify a second tier, including rapidly growing providers. srgresearch	In Q2 2026, AWS held 28%, Microsoft 20%, and Google 15% of worldwide cloud infrastructure services revenue—a combined 63%. A substantial remainder still belonged to other providers. srgresearch

Most capital-intensive businesses ultimately settle into a market structure that is highly concentrated. That might suggest eventual consolidation. But not all capital-intensive industries wind up concentrated. Sometimes, fragmentation persists, as in the cement and oil well businesses. 

Industry	Capital-intensive assets	Leadership structure	Illustrative leaders	Evidence
Semiconductor foundry services, global	Wafer fabrication plants, advanced process equipment, packaging facilities	Highly concentrated; dominant leader	TSMC; Samsung Foundry a distant second	Counterpoint reports Q2 2026 revenue shares of 73% for TSMC and 7% for Samsung Foundry. This concerns foundry services, not the entire semiconductor industry or all internally manufactured chips. counterpointresearch
Large commercial jet aircraft, global	Aircraft development programs, tooling, production facilities, certification investment	Highly concentrated; effective duopoly	Airbus, Boeing	The large jet airliner market has been characterized as a duopoly since the 1990s. The definition excludes treating regional aircraft, business jets, and every emerging competitor as equivalent markets. wikipedia
Cloud infrastructure services, global	Data centers, servers, accelerators, networking and power infrastructure	Concentrated leadership with a substantial competitive fringe	AWS, Microsoft Azure, Google Cloud	The three accounted for 63% of worldwide cloud infrastructure services revenue in Q2 2026: 28%, 20%, and 15%, respectively. Other providers collectively retained 37%. srgresearch
Container shipping, global liner capacity	Container ships, container fleets and supporting logistics infrastructure	Concentrated oligopoly	MSC, Maersk, CMA CGM, COSCO, Hapag-Lloyd	A September 2025 industry compilation puts the top five at approximately 65% of global container capacity. Capacity share is not the same as revenue share or concentration on an individual trade lane. freightender
Crude steel production, global	Blast furnaces, electric-arc furnaces, rolling mills and related facilities	Relatively fragmented globally	China Baowu, ArcelorMittal, Nippon Steel, Ansteel and numerous others	In 2025, the largest producer, China Baowu, produced 124.76 million tonnes against approximately 1.85 billion tonnes worldwide—roughly 6.7%. Very large plants and companies therefore coexist with low global single-firm concentration .worldsteel
Cement manufacturing, global	Kilns, grinding plants, quarries and distribution facilities	Fragmented globally	CNBM, Anhui Conch, Heidelberg Materials, Holcim and other international and domestic groups	Industry analysis explicitly distinguishes global fragmentation from substantially more concentrated local markets. A global classification should not be interpreted as evidence of intense competition around every plant. mckinsey
Cement manufacturing, local markets	The same production assets, serving an economically reachable customer area	Often locally concentrated	A mixture of multinational suppliers and local leaders	McKinsey estimates an average of only three to four competitors in local markets. This is an important example of the same capital-intensive industry being fragmented globally but concentrated where customers actually purchase. mckinsey
Oil and natural gas production, U.S. Lower 48	Wells, drilling and completion investment, gathering systems and processing assets	Numerous producers, with production weighted toward larger firms	Publicly traded producers plus a large private-operator population	EIA identified approximately 12,000 producers in 2025. Public companies represented only 2% of that population but produced 68% of oil and natural gas. The evidence demonstrates an extensive producer base, although it does not by itself establish a top-four or top-ten concentration ratio. eia

And sometimes local concentration coexists with global fragmentation. 

Structural consideration	What the comparison shows
Market boundaries	Cement looks fragmented globally but concentrated locally. Defining the market correctly can reverse the classification. mckinsey
Technology and development barriers	Aircraft and foundries show that capital intensity can accompany sharply concentrated technological leadership.
Scale without complete dominance	Cloud infrastructure has three clear leaders, but their combined 63% share still leaves a meaningful market for other suppliers. srgresearch
Many firms versus evenly distributed output	U.S. oil and gas has thousands of producers, yet public companies account for most production. A high firm count does not imply equal economic importance. eia
Large physical assets versus global company share	Steel demonstrates that enormous industrial facilities do not necessarily translate into a dominant global producer. worldsteel

Still, the neocloud opportunity has been based on compute capacity scarcity, which should ease over time, despite some concerns in the U.S. markets about the possible impact of political forces seeking to slow down or halt some construction of data centers. 

Whether the neocloud industry persists is a reasonable question.